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Lexicon-Driven Handwritten Word Recognition Using Optimal Linear Combinations of Order Statistics
January 1999 (vol. 21 no. 1)
pp. 77-82

Abstract—In the standard segmentation-based approach to handwritten word recognition, individual character-class confidence scores are combined via averaging to estimate confidences in the hypothesized identities for a word. We describe a methodology for generating optimal Linear Combination of Order Statistics operators for combining character class confidence scores. Experimental results are provided on over 1,000 word images.

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Index Terms:
Lexicon-driven, handwritten word recognition, linear combination of order statistics, dynamic programming, normalized edit distance, fuzzy integrals.
Citation:
Wen-Tsong Chen, Paul Gader, Hongchi Shi, "Lexicon-Driven Handwritten Word Recognition Using Optimal Linear Combinations of Order Statistics," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 1, pp. 77-82, Jan. 1999, doi:10.1109/34.745738
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